The only realistic way to counter the adverse effects of search engine bias is to use more SEs (i.e. meta searching) (Mowshowitz Kawaguchi, 2002)

MetaCrawler analysis of relevance rankings (Selberg Etzioni, 1997)

Vivisimo automatic clustering (Palmer et al., 2001)

15Review on Document Visualization 16Document Visualization

Getting insight into information obtained from one or more documents, but without reading those documents (Wise et al., 1995)

Involves three stages (Spence, 2001)

Analysis Extract useful attributes from documents Algorithm Cluster similar documents and reduce dimensionality of the original representationVisualization Displaying the encoded data in a visual format 17Analysis

A search of business intelligence on INSPEC returns 281 article abstracts published between 1969 and 2002

9 key terms/topics were manually identified (based on their importance in the abstracts) from these abstracts

knowledge management, database technology, CRM, ERP, etc.

Assemble business Web sites by meta-searching

A total of 700 business Web sites were collected from 7 major search engines with the 9 key terms as queries

After removing duplicates and filtering, 3,149 pages from 2,860 business Web sites were collected

Non-website pages are filtered out

33Three browsing methods Result list, Web community, and Knowledge map are provided.Users can choose a business intelligence topic here to browse. 34Automatic Parsing and Indexing

Automatically extract key words and hyperlinks from the Web pages

Remove stop words and identify term type

title, heading, content text, and image alternate text

Used Arizona Noun Phraser to automatically extract and index all the noun phrases from each Web page (Tolle Chen, 2000)

Term frequency measures how often a particular term occurs in a Web page

Inverse Web page frequency indicates the specificity of the term

35Co-occurrence Analysis (1)

The similarity between every pair of Web sites (site i and site j) contains the content and structural (connectivity) information. We modify the algorithm used in (He et al., 2001) to find the similarity.

45Result List 46Web CommunityGroups of Web sites organized in hierarchical communitiesClicking on any nodes immediately below the root will open that sub-treeClicking this button, users can open a Web site when they have specified it.Back button allows users to traverse upward in the tree.Panel showing details on demand (labels, title, summary, URL) 47The closeness of any two points reflects their similarityDetails of this Web site is being shown on the bottom panelUsers can control the number of Web sites to be displayedPanel showing details (title, URL summary)Navigation buttons allow browsing in four directionsZooming buttons allow zoom-in or zoom-out functions Knowledge Map 48Kartoo Map 49Experiment Tasks

Designed according to TREC tasks (Voorhees Harman, 1997)

For each of the 4 browsing tools

Task 1 Find Web site information for two companies stated in the question (2 questions, 4 minutes in total)

Close-ended task, requires specific matching

e.g. Find out the URL and the major business areas of Gensym Corporation

Task 2 Find Web site information relevant to a topic stated in the question (1 question, 8 minutes in total)

Open task, requires to find similar or relevant results

e.g. Find out the titles and URLs of the Web sites that are related to CRM benchmarking

50Experiment design

Comparisons

Web community vs. Result list

Knowledge map vs. Web community

Knowledge map vs. Kartoo map

One-factor repeated-measures design

The content of the tasks were different for different browsing methods but their natures were the same (i.e. close-ended for task 1 and open-ended for task 2)

In terms of effectiveness, efficiency and users ratings on the meaning of point placement

Reasons KMs intuitive meaning of point placement, and providing details on demand

Subject 9 This is an intelligent tool and has features superior to any other search as it gives a visual picture of the topic and all topics closely related to the one under search. The map is intuitive and helps steer the user to the right topics or the ones that are close.

Other algorithms can be applied in the different steps of document visualization

Co-occurrence analysis, clustering, MDS

New visualization metaphors can be developed for Web browsing

Other domain areas can be explored to create knowledge maps and discovering communities

Organizations with scattered and voluminous data

72Thank you very much!

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